Joint Optimization of Trajectory, Offloading, Caching, and Migration for UAV-Assisted MEC

UAV-assisted MEC revolutionizes edge computing by deploying UAVs for real-time data processing in areas lacking infrastructure, supporting a wide range of applications from emergency responses to smart cities. Unlike edge servers, UAVs face substantial computational constraints, necessitating a comprehensive strategy that integrates UAV trajectory with task offloading, caching, and migration. Existing studies often overlook the synergy among these strategies, impacting their overall effectiveness. Furthermore, the focus on content pre-caching overlooks task caching's critical role in addressing high computational demands with limited UAV resources. This research aims to jointly optimize UAV trajectories and task management strategies, including offloading, caching, and migration. Utilizing the Lyapunov optimization framework, we break down the complex optimization problem into manageable subproblems: UAV placement, user-UAV association, task offloading, scheduling, and bandwidth allocation, addressed iteratively using the Block Coordinate Descent method. Specifically, the scheduling subproblem is transformed into a non-convex quadratically constrained quadratic programming problem, managed effectively through semidefinite relaxation and a probabilistic mapping approach. Our simulations show that this integrated approach significantly boosts system throughput and reduces execution times compared to conventional methods. This study enhances the understanding of the interplay between UAV trajectory planning and task management, offering vital theoretical insights for advancing UAV-assisted MEC systems.